Analysis of biological information is used for authentication of a person. This analysis largely known as “biometrics” includes sensing, measurement, and quantification of the biological information of person. Accordingly, biometrics includes sensing and measurement of physical or anatomical characteristics which are typically unique to person and certain dynamic quantities, which may be physiological, have variable quantitative nature. Examples of biometrics include fingerprints, retinal scans, speaker recognition, signature recognition, hand recognition and others.
Recognition of multiple biological characteristics information has primordial significance in the various areas e.g., authentication, robotics, personal entertainment and gaming.
Authentication is a process of verifying that the user is who they claim to be. A goal of biometric verification is to determine if the user is the authentic enrolled user or an impostor. Generally, biometric verification includes capturing human input, filter out unwanted input such as noise, statistical analysis of the biometric input and matching against biometric information previously gathered and stored during an enrollment procedure.
The use of one of these physical characteristics for recognizing an individual is termed as unimodal verification system. On the contrary, if multiple physical characteristics are combined to decide the identity, it is called a multi-modal verification system.
A multimodal biometric system allows the integration of two or more types of biometric recognition and verification systems in order to meet stringent performance requirements. An example of a multimodal system may include a combination of fingerprint verification, face recognition, and voice verification. Multimodal biometric systems may be used to take advantage of the proficiency of each individual biometric and help overcome some of the limitations of a single biometric.
Multi-modal refers to multiple sources of data from which identification can be made. The sources of data can be different features of an entity to be identified such as retinal scan (“iris”), fingerprint, voice, facial features, handwriting, vein analysis, or the like. These multimodal systems are used today worldwide in various applications such as car-infotainment systems, robotics applications and gaming applications. For example, mobile devices are recently being designed to take speech and gesture movements as inputs for exploring various applications on the mobile device. Further, there are variety of interactive games that uses multimodal data as inputs for taking various decisions while playing these games. One of the well-know interactive game Kinect™ uses the depth information as an input.
In a conventional multi-modal verification system, the multiple modalities of a person to be verified are obtained and fusion of these multimodal inputs received is done by the system. The fusion of multimodal inputs is done at three levels namely sensor, feature and decision or score level. The multimodal inputs are analyzed at different instance and for each instance of multimodal input, a score is generated for each of the inputs. The decision to accept or reject an individual is made based on the maximum probable value for each of the said inputs.
Thus, the conventional multi-modal system at certain instances may lack accuracy in the identification of an individual due to ignorance of modality input that has less probable values. Hence, there is a need for improved multi-modal verification system that provides more accuracy in recognizing the individual to be verified.
Efforts have been made in the past for improving the multi-modal verification system using biometrics as modalities.
However, existing approaches to address the problem of enhancing the accuracy of recognition is limited to biometric modality inputs. That is they are not capable of handling the non-biometric inputs such as hand gestures. The performance of existing solutions in the art may degrade in real unconstrained environments such as varying lightning conditions, non-uniform distance between the camera and the entity/person/individual/subject to be verified, variable image resolution, blur factors etc. Further, the known systems and methods lack determination of association of one instance of a modality input with the instance of another modality input.
Thus, in the light of the above, it is evident that, there is a need for system and method that provides a novel decision module architecture that improves the overall accuracy of the subject authentication by:                Establishing association between at least one instances of modality input with the instance of other modality input.        Enhancing the performance of the multi-modal verification system even in unconstrained environmental conditions.        Enabling recognition with modality inputs as non-biometric data.        